Methods and systems for ordered food preferences accompanying symptomatic inputs

ABSTRACT

A system for ordered food preferences accompanying symptomatic inputs, the system including a computing device, the computing device designed and configured to retrieve a food profile pertaining to a user; select a first food element as a function of the food profile; select a second food element as a function of the first food element; create a food preference menu wherein the food preference menu contains the first food element and the second food element; and modify the food preference menu as a function of an entry contained within a symptomatic database.

CROSS-REFERENCE TO RELATED APPLICATIONS

This continuation application claims the benefit of priority of U.S.Non-Provisional patent application Ser. No. 16/887,319 filed on May 29,2020 and entitled “METHODS AND SYSTEMS FOR ORDERED FOOD PREFERENCESACCOMPANYING SYMPTOMATIC INPUTS,” which is incorporated by referenceherein in its entirety.

FIELD OF THE INVENTION

The present invention generally relates to the field of nourishment. Inparticular, the present invention is directed to methods and systems forordered food preferences accompanying symptomatic inputs.

BACKGROUND

Food element preferences can change over time. In addition, variousfeatures can affect food element preferences. Utilizing food preferencesin combination with selecting food elements that minimize symptomaticinputs remain to be seen.

SUMMARY OF THE DISCLOSURE

In an aspect, a system for ordered food preferences accompanyingsymptomatic inputs, the system comprising a computing device, thecomputing device designed and configured to retrieve a food profilepertaining to a user; select a first food element as a function of thefood profile; select a second food element as a function of the firstfood element; create a food preference menu wherein the food preferencemenu contains the first food element and the second food element; andmodify the food preference menu as a function of an entry containedwithin a symptomatic database.

In an aspect, a method for ordered food preferences accompanyingsymptomatic inputs, the method comprising retrieving by a computingdevice, a food profile pertaining to a user; selecting by the computingdevice, a first food element as a function of the food profile;selecting by the computing device, a second food element as a functionof the first food element; creating by the computing device, a foodpreference menu wherein the food preference menu contains the first foodelement and the second food element; and modifying by the computingdevice, the food preference menu as a function of an entry containedwithin a symptomatic database.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram illustrating an exemplary embodiment of asystem for ordered food preferences accompanying symptomatic inputs;

FIG. 2 is a block diagram illustrating an exemplary embodiment of asymptomatic database;

FIG. 3 is a diagrammatic representation of a food element list;

FIG. 4 is a process flow diagram illustrating an exemplary embodiment ofa method of ordered food preferences accompanying symptomatic inputs;and

FIG. 5 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations, and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed tosystems and methods for ordered food preferences accompanyingsymptomatic inputs. In an embodiment, symptomatic input is utilized togenerate a food preference menu. Genetic food preferences are utilizedin combination with a machine-learning process to identify foodpreferences, and rank food elements utilizing such information.

Referring now to FIG. 1 , an exemplary embodiment of a system 100 forordered food preferences accompanying symptomatic inputs is illustrated.System 100 includes a computing device 104. Computing device 104 mayinclude any computing device 104 as described in this disclosure,including without limitation a microcontroller, microprocessor, digitalsignal processor (DSP) and/or system on a chip (SoC) as described inthis disclosure. Computing device 104 may include, be included in,and/or connect with a mobile device such as a mobile telephone orsmartphone. Computing device 104 may include a single computing device104 operating independently or may include two or more computing device104 operating in concert, in parallel, sequentially or the like; two ormore computing devices 104 may be included together in a singlecomputing device 104 or in two or more computing devices 104. Computingdevice 104 may interface or connect with one or more additional devicesas described below in further detail via a network interface device.Network interface device may be utilized for connecting computing device104 to one or more of a variety of networks, and one or more devices.Examples of a network interface device include, but are not limited to,a network interface card (e.g., a mobile network interface card, a LANcard), a modem, and any combination thereof. Examples of a networkinclude, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an association, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices 104, and any combinations thereof. A network may employ a wiredand/or a wireless mode of communication. In general, any networktopology may be used. Information (e.g., data, software etc.) may betransmitted to and/or from a computer and/or a computing device 104.Computing device 104 may include but is not limited to, for example, acomputing device 104 or cluster of computing devices 104 in a firstposition and a second computing device 104 or cluster of computingdevices 104 in a second position. Computing device 104 may include oneor more computing devices 104 dedicated to data storage, security,dispersal of traffic for load balancing, and the like. Computing device104 may distribute one or more computing tasks as described below acrossa plurality of computing devices 104 of computing device 104, which mayoperate in parallel, in series, redundantly, or in any other manner usedfor dispersal of tasks or memory between computing devices 104.Computing device 104 may be implemented using a “shared nothing”architecture in which data is cached at the operative, in an embodiment,this may enable scalability of system 100 and/or computing device 104.

Continuing to refer to FIG. 1 , computing device 104 may be designedand/or configured to perform any method, method step, or sequence ofmethod steps in any embodiment described in this disclosure, in anyorder and with any degree of repetition. For instance, computing device104 may be configured to perform a single step or sequence recurrentlyuntil a desired or commanded outcome is achieved; repetition of a stepor a sequence of steps may be performed iteratively and/or recursivelyusing outputs of previous repetitions as inputs to subsequentrepetitions, assembling inputs and/or outputs of repetitions to producean aggregate result, reduction or decrement of one or more variablessuch as global variables, and/or division of a larger processing taskinto a set of iteratively addressed smaller processing tasks. Computingdevice 104 may perform any step or sequence of steps as described inthis disclosure in parallel, such as simultaneously and/or substantiallysimultaneously performing a step two or more times using two or moreparallel threads, processor cores, or the like; division of tasksbetween parallel threads and/or processes may be performed according toany protocol suitable for division of tasks between iterations. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which steps, sequences of steps, processingtasks, and/or data may be subdivided, shared, or otherwise dealt withusing iteration, recursion, and/or parallel processing.

With continued reference to FIG. 1 , computing device 104 is configuredto receive a symptomatic input 108 relating to a user. A “symptomaticinput,” as used in this disclosure, is a description of any physicaland/or mental feature of a user that may indicate a condition and/ordisease. A symptomatic input 108 includes a description of a symptomaticcomplaint. A “symptomatic complaint,” as used in this disclosure, is adescription of any symptom a user has previously or is currentlyexperiencing. A symptomatic complaint may include a symptom of a diseasethat a user experiences, such as a user who experiences joint pain andfatigue from rheumatoid arthritis. In yet another non-limiting example,a symptomatic complaint may contain a description of symptoms that auser who was previously diagnosed with fibroids experiences whichincludes heavy menstrual bleeding, pelvic pressure, and frequenturination. A symptomatic input may include a subjective symptom such astiredness. A symptomatic input 108 may include an objective symptom suchas a cough. A symptomatic input 108 may indicate a physiological stateof a user, such as a pregnant female who complains of morning sickness.A symptomatic input 108 may describe a brief acute symptom, such as aback spasm that comes on suddenly. A symptomatic input 108 may describea chronic symptom, such as a dry cough and chest congestion that occursevery morning upon waking. A symptomatic complaint may include adescription of one or more general symptoms that affect the entire bodysuch as fever, malaise, anorexia, and weight loss.

With continued reference to FIG. 1 , a symptomatic complaint may includea diagnosis. A “diagnosis,” as used in this disclosure, is a disease,syndrome, condition, disorder, sickness, ailment, and the likeidentified by a professional. A “professional,” as used in thisdisclosure, is any person with valid credentials and/or certificationsto provide wellness services to natural persons. A professional mayinclude a physician, a dentist, a nurse, a chiropractor, an optometrist,a physical therapist, an occupational therapist, a dietician, a nursepractitioner, a psychologist, a licensed professional counselor, alicensed marriage and/or family therapist, a pharmacist, a speechtherapist and the like. A diagnosis may include a current condition thata user suffers from, such as ulcerative colitis. A diagnosis may includean impending condition that a user may be at danger of developing due toone or more features, such as heart disease or Type 2 Diabetes Mellitus.A diagnosis may include a condition that was cured and/or resolved, suchas an ear infection or a meningitis. A diagnosis may be self-reported bya user, such as a user who self-reports a previous diagnosis ofhypertension that a user is currently treating with a drug. Asymptomatic input 108 may include an element of previous physical data.An “element of previous physical data,” as used in this disclosure, isany previous therapeutic and/or wellness information unique to a user.An element of previous physical data may include demographic informationthat may include a user's name, gender, age, birthday, occupation,family structure, living arrangements, marital status, and the like. Anelement of previous physical data may include information regarding anyinformation regarding symptomatic complaints regarding specific bodysystems such as the head, eyes, ears, nose, and throat (HEENT),cardiovascular, respiratory, gastrointestinal, genitourinary,integumentary, musculoskeletal, endocrine, nervous system, mental, andthe like. An element of previous physical data may relate to informationdescribing a user's social well-being and social life, family history,mental or emotional illness or stresses, detrimental or beneficialhabits, social life, smoking or exercise habits, educational level,previous surgical history, previous procedures, culture, sexuality,nutrition, spirituality, and the like. An element of previous physicaldata may relate to past wellness history such as allergies, serious orchronic illnesses, recent hospitalizations, recent surgical procedures,current drugs, alcohol consumption, marijuana use and the like. Anelement of previous physical data may be received based on one or morequestions and/or self-assessments completed by a user.

With continued reference to FIG. 1 , information pertaining to asymptomatic input 108 may be stored within symptomatic database 112.Symptomatic database 112 may be implemented, without limitation, as arelational database, a key-value retrieval datastore such as a NOSQLdatabase, or any other format or structure for use as a datastore that aperson skilled in the art would recognize as suitable upon review of theentirety of this disclosure.

With continued reference to FIG. 1 , computing device 104 may receive asymptomatic input 108 relating to a user from a user device 116 operatedby a user. A user device 116 may include without limitation, a displayin communication with computing device 104, where a display may includeany display as described herein. A user device 116 may include anadditional computing device, such as a mobile device, laptop, desktop,computer, and the like.

With continued reference to FIG. 1 , computing device 104 is configuredto generate a machine-learning process, wherein the machine-learningprocess is trained using a first training set relating symptomaticinputs to symptomatic neutralizers. A “machine-learning process,” asused in this disclosure, is a process that automatedly uses a body ofdata known as “training data” and/or a “training set” to generate analgorithm that will be performed by computing device 104 and/or a moduleto produce outputs given data provided as inputs; this is in contrast toa non-machine learning software program where the commands to beexecuted are determined in advance by a user and written in aprogramming language. “Training data,” as used in this disclosure, is aset of examples that contain pairs of an input and a correspondingoutput, which are used to model relationships between two or morecategories of data elements. Training data may be formatted to includelabels, for instance by associating data elements with one or moredescriptors corresponding to categories of data elements. Training datamay not contain labels, where training data may not be formatted toinclude labels. A machine-learning process 120 may include calculatingone or more machine-learning algorithms and/or producing one or moremachine-learning models. A machine-learning process 120 may include asupervised machine-learning process 120 that applies learnedassociations from the past to new data using labeled training data topredict future events. A supervised machine-learning process 120produces an inferred function to make predictions about output values. Asupervised machine-learning process 120 may include for example, activelearning, classification, regression, and/or similarity learning. Amachine-learning process 120 may include an unsupervisedmachine-learning process 120 where training data utilized to train theunsupervised machine-learning process 120 may not be classified orlabeled. An unsupervised machine-learning process 120 may infer afunction to describe a hidden structure from unlabeled data. Anunsupervised machine-learning process 120 may include for example,clustering, anomaly detection, neural networks, latent variable models,and the like. A machine-learning process 120 may include asemi-supervised machine-learning process 120 that may utilize acombination of both labeled and unlabeled training data. Asemi-supervised machine-learning process 120 may include generativemodels, low density separation, graph-based methods, heuristicapproaches, and the like. A machine-learning process 120 may beimplemented as any machine-learning process, including for instance, andwithout limitation, as described in U.S. Nonprovisional application Ser.No. 16/375,303, filed on Apr. 4, 2019, and entitled “SYSTEMS AND METHODSFOR GENERATING ALIMENTARY INSTRUCTION SETS BASED ON VIBRANTCONSTITUTIONAL GUIDANCE,” the entirety of which is incorporated hereinby reference.

With continued reference to FIG. 1 , training data utilized to train amachine-learning process 120 may be obtained from records of previousiterations of a machine-learning process 120, user inputs and/orquestionnaire responses, expert inputs, open source platforms and thelike. Computing device 104 trains a machine-learning process 120including any machine-learning algorithm and/or any machine-learningmodel utilizing a first training set relating symptomatic inputs to foodpreferences.

With continued reference to FIG. 1 , computing device 104 is configuredto identify a symptomatic neutralizer based on a symptomatic input usinga machine-learning process. A “food element,” as used in thisdisclosure, is any food, beverage, drink, snack, nutritional supplement,and the like intended for consumption by a human being. A “symptomaticneutralizer,” as used in this disclosure, is any indication as to howmuch or how little a food element 124 helps alleviate or exacerbate asymptomatic input 108. For instance and without limitation, a foodelement 124 such as organ meats may exacerbate a condition of gout,while a food such as sauerkraut may alleviate symptoms associated with acondition such as irritable bowel syndrome. A symptomatic neutralizermay be expressed as a quantitative datum, containing a numerical scorethat indicates how much or how little a food element alleviates orexacerbates a symptomatic input 108. A symptomatic neutralizer may beexpressed on a numerical score from 0 to 100 for example, where a scoreof 0 may indicate a food element that exacerbates a symptomatic input,while a score of 100 may indicate a food element that alleviates asymptomatic input. For instance and without limitation, a symptomaticneutralizer for a food element such as tomatoes may contain aquantitative datum containing a numerical score of 17 for exacerbatingsymptoms of a stomach ulcer, while a symptomatic neutralizer for a foodelement such as honey may contain a quantitative datum containing anumerical score of 77 for alleviating symptoms of a stomach ulcer.Computing device 104 generates a symptomatic neutralizer utilizing oneor more regression processes, including any of the regression processesas described herein.

With continued reference to FIG. 1 , computing device 104 is configuredto generate an ordered food preference 132. An “ordered foodpreference,” as used in this disclosure, is a list of food element 124ranked based on how well each food element 124 diminishes a symptomaticinput 108. A food element 124 may help diminish a symptomatic input 108when the food element 124 helps alleviate, lessen, treat, and/ormitigate a symptomatic input 108. Food element 124 may be ranked indescending order, whereby food element 124 that help diminish asymptomatic input 108 the most are ranked first, and those that worsen asymptomatic input 108 are ranked last. For instance and withoutlimitation, a symptomatic input 108 that contains a diagnosis of aurinary tract infection may be utilized to generate an ordered foodpreference 132 that specifies a food element 124 such as raw cranberriesas having a high ranking and being able to diminish symptoms of aurinary tract infection, while a food element 124 such as cranberryjuice cocktail beverage may have a very low ranking as it contains highlevels of high fructose corn syrup that will exacerbate and worsensymptoms of a urinary tract infection. In yet another non-limitingexample, a symptomatic input containing a user with an increased risk ofdeveloping breast cancer, may contain an ordered food preference 132that specifies a food element 124 such as all cruciferous vegetablesincluding broccoli, Brussel sprouts, cauliflower, cabbage, bok choy,radish, arugula, and kohlrabi as having a high ranking and reducing auser's risk of developing breast cancer, while a food element 124 suchas red meat may exacerbate a user's risk of developing breast cancer.

With continued reference to FIG. 1 , computing device 104 generates anordered food preference 132 utilizing a food preference indicator 136. A“food preference indicator,” as used in this disclosure, is anyevaluative attitude that a user expresses towards any food element 124.A food preference may include a qualitative evaluation of foods andbeverages, indicating how much a user likes or dislikes them. A foodpreference may be determined based on a hedonic scale, indicating howmuch a user likes a food or how much a user dislikes a food. A foodpreference may be determined on a preferred frequency scale, such as howoften does a user eat or consume a particular food or beverage. A foodpreference may be based on a numerical scale, indicating on a scale from0 to 10 how much a user likes or dislikes a food, where a score of 0indicates a food that a user does not like and 10 indicates a food thata user does like. One or more food preference indicator 136 may bestored in symptomatic database 112, as described above in more detail.For instance and without limitation, a food preference indicator 136 mayspecify that a user prefers to consume vegetables that include carrots,celery, and romaine lettuce, but the user does not like to consumeBrussel sprouts or cabbage. In yet another non-limiting example, a foodpreference indicator 136 may specify that a user dislikes all animalproducts and enjoys consuming gluten free vegan foods. Computing device104 utilizes a food preference to generate an ordered food element list.For instance and without limitation, computing device 104 may rank afood element 124 contained within an ordered food element list higherwhen the food element 124 is a food element 124 the user likes toconsume, and the food element 124 is a symptomatic neutralizer. In yetanother non-limiting example, computing device 104 may rank a foodelement 124 lower when a food element 124 is a food element 124 that theuser does not like to consume and the food element 124 is a symptomaticneutralizer 128, because the user is most likely not going to consumethe food element 124.

With continued reference to FIG. 1 , computing device 10 is configuredto identify an element of data containing a genetically related foodpreference 140 for a user. A “genetically related food preference,” asused in this disclosure, is any genetic indication and/or predispositionthat affects a user's food preferences and/or food element 124 tastes. Agenetic indication may include the absence and/or presence of any genesthat may control a user's food preferences. For instance and withoutlimitation, a user's taste preferences for foods that are sweet tastingmay be implicated by genes that are involved in glucose metabolismincluding but not limited to, TAS1R1, TAS1R2, TAS1R3, SLC2A2, ADIPOQ,ANKK1, DRD2, OPRM1, LEP, LEPR, NPY1 and the like. In yet anothernon-limiting example, a user's taste preferences for foods that containfats may be controlled by polymorphisms in the CD36 gene that encodefatty acid translocase, as well as genes that affect regulation oflipolysis and thermogenesis, lipoprotein metabolism, neurotransmission,and signaling regulation such as but not limited to, ADRB3, APOA2,OPRM1, RGS6 and the like. One or more genetically related foodpreferences pertaining to a user may be stored in symptomatic database112. Computing device 104 utilizes a genetically related food preference140 to generate an ordered food preference 132. For example, a foodelement 124 such as coffee may be ranked higher for a user with agenetically related food preference 140 for bitter foods as affected bythe TAS2R38 gene, as compared to a user who does not have a geneticallyrelated food preference 140 for bitter foods. In yet anothernon-limiting example, a food element 124 containing a savory foodelement 124 such as green cabbage may be ranked higher for a user with agenetically related food preference 140 for umami foods as affected by aGNAT3 gene co-expressed with TAS1R1 gene, as compared to a user who doesnot have co-expression of the GNAT3 gene and the TAS1R1 gene.

With continued reference to FIG. 1 , computing device 104 generates anordered food preference 132 utilizing a social conduct factor 144. A“social conduct factor,” as used in this disclosure, is any socialimpact on a user's food preferences. A social impact may include anyfoods and/or cuisines that a user was exposed to as a child, and thatshaped a user's food preferences. For instance and without limitation, auser who was brought up in a household eating only vegetarian meals maynot eat meat or feel comfortable eating food element 124 that containmeat. A social impact may include any food behaviors and/or foodpreferences that a user developed based on food element 124 that afamily member or friend likes. For example, a husband may learn overtime to enjoy food element 124 that his wife enjoys such as fresh saladsor meal that contain chicken as compared to beef. In yet anothernon-limiting example, a user who lives with multiple friends togethermay learn over time to enjoy food element 124 that the user's friendsalso like. A social influence may include any influences regarding typesof food element 124 that a user consumes. For example, a user whoroutinely eats out at restaurants and who doesn't cook meals at home maybe more adventurous regarding food items and may consume a wider varietyof food element 124 as compared to someone who exclusively cooks mealsat home. A social influence may include any social eating patterns oreating habits that a user has developed. For example, a user who worksvery long hours may skip breakfast and only eat two meals each day.Computing device 104 utilizes a social conduct factor 144 to rank foodelement 124. For example, a social conduct factor 144 that indicates auser doesn't eat breakfast may be utilized to rank food element 124 muchlower that are typically food element 124 consumed for breakfast such asoatmeal, eggs benedict, pancakes, donuts, and bacon. One or more socialimpacts may be stored within symptomatic database 112.

With continued reference to FIG. 1 , computing device 104 is configuredto receive a prior food preference input 148. A “prior food preferenceinput,” as used in this disclosure, is a description of any previousfood element 124 that a user has consumed. A prior food preference input148 may contain a description of a food element 124 that a userconsumed, such as a snack containing almonds that a user ate. A priorfood preference input 148 may contain a description of a meal that auser consumed, such as a meal containing grilled flank steak served on abed of arugula and with a side of avocado. A prior food preference input148 may contain a description of a user's previous eating patterns, suchas a description of certain cuisines that a user enjoys eating,including Italian inspired meals or Japanese meals. A prior foodpreference input 148 may be stored within symptomatic database 112.Computing device 104 classifies using a Naïve Bayes classifier, a priorfood preference input 148 to a food profile. A “classifier,” as used inthis disclosure, is a process whereby computing device 104 derives fromtraining data, a model known as a “classifier” for sorting inputs intocategories or bins of data. A classifier utilizes a prior foodpreference input 148 as an input, and outputs a food profile 156.Computing device 104 trains classifier, utilizing training data.Training data may be obtained from records of previous iterations of aclassifier, user inputs and/or questionnaire responses, expert inputs,and the like. A Naïve Bayes classifier 152, utilizes a family ofalgorithms to assign class labels to problem instances, represented asvectors of feature values, where class labels are derived from a finiteset. A Naïve Bayes classifier 152 may generate classes, by calculatingan estimate for a class probability from a training set. A Naïve Bayesclassifier 152 may include generating a Gaussian Naïve Bayes classifier152, that may be generated based on an assumption that continuous valuesassociated with each class are distributed according to a normal orGaussian distribution. A Naïve Bayes classifier 152 may includegenerating a multinomial Naïve Bayes classifier 152, where featurevectors represent the frequencies with which certain events have beengenerated by a multinomial. A Naïve Bayes classifier 152 may includegenerating a Bernoulli Naïve Bayes classifier 152, where features thatare independent Boolean binary variables describe inputs.

With continued reference to FIG. 1 , computing device 104 classifies aprior food preference input 148 to a food profile 156. A “food profile,”as used in this disclosure, is a compilation of food elements 124containing an associated datum indicating whether or not each foodelement is recommended for a user. A food profile 156 may contain foodelements 124 that are similar to food elements 124 contained within aprior food preference input 148. For example, a prior food preferenceinput 148 that contains a meal containing salmon may be classified to afood profile 156 that recommends other fish choices similar to salmonincluding Arctic char, Ocean trout, Amber Jack, Mackerel, Wahoo, StripedBass, Milkfish, and Bluefish. A food profile 156 may contain foodelement 124 that may pair well with food element 124 contained within aprior food preference input 148. For example, a prior food preferenceinput 148 that contains a chocolate cake may be utilized to recommend ina food profile 156 other desserts containing chocolate such as chocolatecupcakes, chocolate ice cream, and chocolate pudding. A prior foodpreference input 148 may be evaluated to determine food element 124 thata user will dislike based on ingredients contained within a prior foodpreference input 148. For example, a prior food preference input 148that contains only vegetarian entrees may be utilized to classify theuser to a food profile 156 that contains only vegetarian food element124 recommendations. In yet another non-limiting example, a prior foodpreference input 148 that contains only lactose free dairy products maybe utilized to classify the user to a food profile 156 that containslactose free dairy products. Computing device 104 utilizes a foodprofile 156 to generate an ordered food element list. Food element 124contained within a food profile 156 may be utilized to order foodelement 124. For example, a food profile 156 that contains vegan foodelement 124 may be utilized by computing device 104 to generate anordered food element list that ranks vegan food element 124 higher thananimal containing food element 124. In yet another non-limiting example,a food profile 156 that contains food element 124 of a certain ethnicitymay be utilized by computing device 104 to generate an ordered foodelement list that ranks food element 124 matching the ethnicity higherthan food element 124 of other ethnicities.

With continued reference to FIG. 1 , computing device 104 is configuredto create a food preference menu for a user wherein creating the foodpreference menu 160 includes grouping ordered food elements. A “foodpreference menu,” as used in this disclosure, is a list of suggestedmeal items for a user. A food preference menu 160 may contain a list ofspecific meals, such as suggested meals for breakfast, lunch, dinner,and/or snacks. A food preference menu 160 may contain a list of foodelement 124 needed to prepare a particular food preference menu 160item. For instance and without limitation, a food preference menu 160may contain a recommended breakfast that contains millet served withcoconut milk and topped with cinnamon, vanilla, and black currants. Inyet another non-limiting example, a food preference menu 160 may containa recommended dinner that includes teriyaki salmon served with whiterice and topped with broccoli, red cabbage, carrots, green onions,avocado, lime, and sprinkled with sesame seeds. Computing device 104 mayutilize an ordered food element list containing food element listrankings, to rank food preference menu 160. For instance and withoutlimitation, a user with a symptomatic input 108 of gout may receive afood preference menu 160 that foods a first meal item such as halibutwith baby potatoes ranked as having a neutral effect on a user'ssymptoms of gout, and a second meal item such as grilled portobellomushroom topped with caramelized onions and served on a bed of millet ashaving a positive effect on a user's symptoms of gout. Computing device104 groups ordered food element 124 to create meals. In an embodiment,computing device 104 groups food element 124 by pairing a first foodelement 124 with a second food element 124. Pairing may includecombining food element 124 based on taste, food preferences, knowncombinations, recipes, and the like. For example, computing device 104may group a first food element 124 such as oysters with a second foodelement 124 such as kiwi fruit. In yet another non-limiting example, afirst food element 124 such as shrimp may be recommended to be pairedwith a second food element 124 such as avocado, however for a user witha dislike of avocado, another food element 124 such as mango may berecommended instead. Computing device 104 may consult symptomaticdatabase 112 to identify a user's food preferences and/or an orderedfood list to determine food element 124 pairings. In an embodiment, auser may select one or more food preference menu 160 items that are ofinterest to the user and/or that the user would consume, and computingdevice 104 may generate a grocery ingredient list. A “grocery ingredientlist,” as used in this disclosure, is a list of food element 124 neededto prepare a food preference menu 160 item. A grocery ingredient listmay be utilized by a user to shop for food element 124 such as in agrocery store or online when ordering groceries to be picked up ordelivered.

With continued reference to FIG. 1 , a food preference menu 160 mayinclude a nourishment strategy. A “nourishment strategy,” as used inthis disclosure, is any nutritional plan recommended for a user. Anutritional plan may include a map of suggested meals assigned tocertain meal slots, times, and/or days of the week. A nutritional planmay be generated over a certain period of time, such as a plan for aday, a week, a month, a year, and the like. In an embodiment, computingdevice 104 may utilize a nourishment strategy to generate a groceryingredient list for a user based on the nourishment strategy.

Referring now to FIG. 2 , an exemplary embodiment 200 of symptomaticdatabase 112 is illustrated. Symptomatic database 112 may be implementedas any data structure suitable for use as described above in more detailin reference to FIG. 1 . One or more tables contained within symptomaticdatabase 112 may include symptomatic input table 204; symptomatic inputtable 204 may include one or more symptomatic input 108 relating to auser. One or more tables contained within symptomatic database 112 mayinclude food preference table 208; food preference table 208 may includeone or more food preferences relating to a user. One or more tablescontained within symptomatic database 112 may include genetic table 212;genetic table 212 may contain information relating to one or moregenetically related food preferences 140 relating to a user. One or moretables contained within symptomatic database 112 may include socialimpact table 216; social impact table 216 may contain informationrelating to one or more social conduct factor 144. One or more tablescontained within symptomatic database 112 may include prior foodpreference table 220; prior food preference table 220 may containinformation relating to a user's prior food preference inputs 148. Oneor more tables contained within symptomatic database may include foodprofile table 224; food profile table 224 may include informationpertaining to a user's food profile 156.

Referring now to FIG. 3 , an exemplary embodiment 300 of food elementlist is illustrated. Computing device 104 generates an ordered foodelement list 304 ranking food element 124. An ordered food element list304 may be generated utilizing any of the methodologies as describedherein. Food element 124 contained within an ordered food element list304 may be ranked utilizing symptomatic neutralizer 128, and the abilityof a food element 124 to reduce and/or help exacerbate a symptomaticinput 108. Food element 124 contained within an ordered foot item list304 may also be ranked based on any food preference indicator 136, anygenetically related food preference 140, and/or any social conductfactor 144. Computing device 104 utilizes an ordered food element listto create a food preference menu 160 for a user. A food preference menu160 may contain suggested meal options for a user. For example, a foodpreference menu 160 may contain suggestions breakfast options, suggestedlunch options, suggested dinner options, and/or any suggested snackoptions. Computing device 104 utilizes a food preference menu 160 togenerate a grocery ingredient list 308. A grocery ingredient list mayinclude any of the grocery ingredient lists as described above in moredetail in reference to FIG. 1 . For instance and without limitation, auser may select one or more suggested meals contained within a foodpreference menu 160 and utilize the suggested meals to generate agrocery ingredient list 308 to be utilized to purchase items in agrocery store or food store for example. Computing device 104 utilizes afood preference menu 160 to generate a nourishment strategy 312,including any of the nourishment strategies 312 as described above inmore detail in reference to FIG. 1 . A nourishment strategy may containinformation mapping suggested meals to particular mealtimes over aspecified period of time.

Referring now to FIG. 4 , an exemplary embodiment 400 of a method ofordered food preference 132 accompanying symptomatic input 108 isillustrated. At step 405, computing device 104 receives a symptomaticinput 108 relating to a user. A symptomatic input 108 includes any ofthe symptomatic input 108 as described above in more detail in referenceto FIG. 1 . Computing device 104 may receive a symptomatic input 108from a user device 116, utilizing any network methodology as describedherein. In yet another non-limiting example, computing device 104 mayreceive a symptomatic input 108 from symptomatic database 112 asdescribed above in more detail in reference to FIGS. 1-2 . A symptomaticinput 108 contains a description of a symptomatic complaint. Asymptomatic complaint includes any of the symptomatic complaints asdescribed above in more detail in reference to FIG. 1 . A symptomaticcomplaint may contain a description of symptoms a user may be currentlyexperiencing such as a headache, runny nose, and fatigue. A symptomaticcomplaint may contain a description of symptoms that come and go, suchas nausea that occurs after eating. A symptomatic complaint may containan element of previous physical data which may include any of theprevious physical data as described above in more detail in reference toFIG. 1 . For example, a symptomatic complaint may contain a descriptionof a user's surgical history which specifies that a user has had threeoperations to try and mend a broken ankle. In yet another non-limitingexample, a symptomatic complaint may contain a description of variousallergies that a user has, such as an allergy to all corn containingproducts. A symptomatic complaint may contain a diagnosis, including anyof the diagnoses as described above in more detail in reference to FIG.1 . For example, a symptomatic complaint may contain a user'sself-reported diagnosis of rheumatoid arthritis. In yet anothernon-limiting example, a symptomatic complaint may contain a diagnosisthat a user previously and that was previously cured and/or resolved.For example, a symptomatic complaint may contain a user's previousdiagnosis of small intestinal bowel overgrowth that a user had for sixmonths, that was resolved following a course of natural anti-microbials.In yet another non-limiting example, a symptomatic complaint may containa description of a previous diagnosis a user had such as a c-difficileinfection that was resolved upon completion of a course of antibiotics.Computing device 104 may store one or more symptomatic complaints withinsymptomatic database 112, as described above in more detail.

With continued reference to FIG. 4 , at step 410, computing device 104generates a machine-learning process wherein the machine-learningprocess is trained using a first training set that relates symptomaticinputs 108 to symptomatic neutralizers 128. A first training set may beobtained from any of the sources as described above in more detail inreference to FIG. 1 . A machine-learning process 120 includes any of themachine-learning processes 120 as described above in more detail inreference to FIG. 1 .

With continued reference to FIG. 4 , at step 415, computing device 104identifies a symptomatic neutralizer 128 based on a symptomatic input108 using a machine-learning process 120. A symptomatic neutralizer 128includes any of the symptomatic neutralizer 128 as described above inmore detail in reference to FIG. 1 . A symptomatic neutralizer containsan indication as to how much or how little a food element 124 helpsalleviate or exacerbate a symptomatic input 108. For example, asymptomatic neutralizer 128 may indicate that a food element 124 such asgarlic may help alleviate a symptomatic input 108 of a common cold,while a food element 124 such as foods containing dairy products mayexacerbate the common cold. In yet another non-limiting example, asymptomatic neutralizer 128 may indicate that a food element 124 such asalcohol may worsen symptoms of nausea, while a food element 124 such aschicken broth may help alleviate symptoms of nausea.

With continued reference to FIG. 4 , at step 420, computing device 104generates an ordered food element list. An ordered food element listincludes any of the ordered food element lists as described above inmore detail in reference to FIG. 1 . Computing device 104 generates anordered food element list utilizing any of the methodologies asdescribed above in more detail in reference to FIG. 1 . Computing device104 may generate an ordered food element list by ranking food element124 that are most likely to help alleviate a symptomatic input 108, foodelement 124 that are neutral in alleviating a symptomatic input 108, andfood element 124 that exacerbate a symptomatic input 108. Computingdevice 104 generates an ordered food element list utilizing symptomaticneutralizer 128 as described above in more detail in reference to FIG. 1. In an embodiment, computing device 104 may generate an ordered foodelement list utilizing a user food preference indicator 136, which maydescribe food element 124 that a user likes and/or dislikes. Computingdevice 104 utilizes a user food preference indicator 136 to rank foodelement 124 within an ordered food element list. For instance andwithout limitation, a user food preference indicator 136 that indicatesa user prefers entrees containing fish over entrees containing meat, maybe utilized to rank food element 124 that contain fish higher than fooditems containing meat, and symptomatic relief. For example, for a userwith gout who prefers fish over meat, computing device 104 may utilizethe information to rank fish very high, because the user enjoys eatingit, and fish will help alleviate some symptoms of gout, while computingdevice 104 may rank meat much lower because it will exacerbate a user'ssymptoms and the user does not enjoy eating it. Computing device 104 maygenerate a food element list utilizing information pertaining to asocial conduct factor 144, which may be stored in symptomatic database112. A social conduct factor 144 includes any of the social conductfactor 144 as described above in more detail in reference to FIG. 1 . Asocial conduct factor 144 may describe a social impact of a user'seating habits. For example, a social conduct factor 144 may containinformation pertaining to any eating habits or eating behaviors that auser may have. For example, a social conduct factor 144 may indicatethat a user dislikes eating breakfast and instead only has a cup ofcoffee in the morning. In yet another non-limiting example, a socialconduct factor 144 may indicate that a user consumes a lot of Frenchstyle food because the user grew up in France and developed eatinghabits similar to those of Fresh nationality.

With continued reference to FIG. 4 , computing device 104 utilizes agenetically related food preference 140 for a user to generate anordered food element list. A genetically related food preference 140includes any of the genetically related food preference 140 as describedabove in more detail in reference to FIG. 1 . A genetically related foodpreference 140 may be stored in symptomatic database 112 as describedabove in more detail in reference to FIG. 1 . Computing device 104evaluates a genetically related food preference 140 to determine foodelement 124 likes and/or food element 124 dislikes that may begenetically linked. For instance and without limitation, a geneticallyrelated food preference 140 may indicate that a user with a copy of anSLC2A2 gene may prefer foods that are sweeter, and as such, computingdevice 104 may rank food element 124 that contain sweet tasting foodssuch as berries, dark chocolate, and sweet potatoes as being morepreferential for user. In such an instance, computing device 104utilizes a genetically related food preference 140 to identify foodelement 124 that a user may prefer, as well as weighing in other factorsthat may affect the ranking of identified food element 124 within anordered food element list.

With continued reference to FIG. 4 , computing device 104 generates anordered food element list utilizing a Naïve Bayes classifier 152.Computing device 104 receives a prior food preference input 148. A priorfood preference input 148 includes any of the prior food preferenceinput 148 as described above in more detail in reference to FIG. 1 . Aprior food preference input 148 contain a description of a meal a userpreviously consumed, such as a breakfast consisting of oatmeal toppedwith flaxseeds and berries. In yet another non-limiting example, a priorfood preference input 148 may contain a description of a series orsequence of meals that a user previously consumed, such as all meals auser consumed for the past week, or all dinners that a user ate for amonth. Information pertaining to a prior food preference input 148 maybe stored within symptomatic database 112. Computing device 104classifies using a Naïve Bayes classifier 152, a prior food preferenceinput 148 to a food profile 156. A Naïve Bayes classifier includes anyof the Naïve Bayes classifier 152 as described above in more detail inreference to FIG. 1 . A Naïve Bayes classifier 152 may be trained usinga second training set relating prior food preference input 148 to foodprofile 156. A second training set may be obtained from any of thesources as described above in more detail in reference to FIG. 1 .Computing device 104 generates an ordered food element list utilizing afood profile 156.

With continued reference to FIG. 4 , at step 425, computing device 104creates a food preference menu, wherein creating the food preferencemenu includes grouping ordered food elements. A food preference menu 160includes any of the food preference menu 160 as described above in moredetail in reference to FIG. 1 . A food preference menu 160 may contain alist of specific meals, such as suggested meals for breakfast, lunch,dinner, and/or snacks. A food preference menu 160 may contain one ormore recommended options for breakfast, one or more recommended optionsfor lunch, one or more recommended options for dinner, and/or one ormore recommended options for snacks. In an embodiment, computing device104 may group a food element 124 to generate a food preference menu 160by pairing a first food preference with a second food preference. Forinstance and without limitation, computing device 104 may pair a firstfood preference that contains a genetically related food preference 140containing a user's genetic preference for salty foods, with a secondfood preference containing a user food preference indicator thatcontains a user's preference for shrimp, so create a food preferencemenu 160 that contains a menu item containing a salt and pepper shrimpentrée. Computing device 104 groups ordered food element 124 to generatea grocery ingredient list. A grocery ingredient list includes any of thegrocery ingredient lists as described above in more detail in referenceto FIG. 1 . For example, a user may receive a transmission fromcomputing device 104 to user device 116 containing a food preferencemenu 160 for a user. A user may select one or more menu items availablewithin food preference menu 160 and transmit the selections back tocomputing device 104 from user device 116, utilizing any networkmethodology as described herein. Computing device may generate a groceryingredient list, containing a list of ingredients that a user would needto acquire to prepare selected menu items. In an embodiment a groceryingredient list may be modified to eliminate ingredients based on whatingredients a user may already have at home. In an embodiment, a groceryingredient list may be displayed on remote device, so that a user couldtake the grocery ingredient list with them to a grocery store or placewhere ingredients may be sold, including any online grocery stores.Computing device 104 utilizes a food preference menu 160 to generate anourishment strategy. A nourishment strategy includes any of thenourishment strategies as described above in more detail in reference toFIG. 1 . A nourishment strategy may contain a list of suggested mealsfor a user, broken down by certain days or times.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 5 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 500 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 500 includes a processor 504 and a memory508 that communicate with each other, and with other components, via abus 512. Bus 512 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Memory 508 may include various components (e.g., machine-readable media)including, but not limited to, a random access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 516 (BIOS), including basic routines that help totransfer information between elements within computer system 500, suchas during start-up, may be stored in memory 508. Memory 508 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 520 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 508 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 500 may also include a storage device 524. Examples of astorage device (e.g., storage device 524) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 524 may be connected to bus 512 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 524 (or one or morecomponents thereof) may be removably interfaced with computer system 500(e.g., via an external port connector (not shown)). Particularly,storage device 524 and an associated machine-readable medium 528 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 500. In one example, software 520 may reside, completelyor partially, within machine-readable medium 528. In another example,software 520 may reside, completely or partially, within processor 504.

Computer system 500 may also include an input device 532. In oneexample, a user of computer system 500 may enter commands and/or otherinformation into computer system 500 via input device 532. Examples ofan input device 532 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 532may be interfaced to bus 512 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 512, and any combinations thereof. Input device 532 mayinclude a touch screen interface that may be a part of or separate fromdisplay 536, discussed further below. Input device 532 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 500 via storage device 524 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 540. A network interfacedevice, such as network interface device 540, may be utilized forconnecting computer system 500 to one or more of a variety of networks,such as network 544, and one or more remote devices 548 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 544,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 520,etc.) may be communicated to and/or from computer system 500 via networkinterface device 540.

Computer system 500 may further include a video display adapter 552 forcommunicating a displayable image to a display device, such as displaydevice 536. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 552 and display device 536 may be utilized incombination with processor 504 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 500 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 512 via a peripheral interface 556. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions, and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. A system for ordered food preferencesaccompanying symptomatic inputs, the system comprising a computingdevice, the computing device designed and configured to: retrieve a foodprofile pertaining to a user; select a first food element as a functionof the food profile; select a second food element as a function of thefirst food element; create a food preference menu wherein the foodpreference menu contains the first food element and the second foodelement, wherein creating the food preference menu comprises:identifying a previous meal selection; classifying, using a Naïve Bayesclassifier derived from training data comprising meal selections datacorrelated to meal category data, the previous meal selection to a mealcategory; and creating the food preference menu as a function of themeal category; and modify the food preference menu as a function of anentry contained within a symptomatic database.
 2. The system of claim 1,wherein the food profile is retrieved as a function of a geneticallyrelated food preference.
 3. The system of claim 1, wherein the firstfood element is selected as a function of a user taste preference. 4.The system of claim 1, wherein the first food element is selected as afunction of a symptomatic input.
 5. The system of claim 1, wherein thesecond food element is selected to enhance the nutrition of the firstfood element.
 6. The system of claim 1, wherein creating the foodpreference menu further comprises generating a machine-learning process,wherein the machine-learning process utilizes the food profile as aninput, and outputs the food preference menu.
 7. The system of claim 6,wherein the machine-learning process further comprises a featurelearning process.
 8. The system of claim 1, wherein modifying the foodpreference menu further comprises adjusting a portion size containedwithin a nutritional plan.
 9. The system of claim 8, wherein adjustingthe portion size further comprises: locating a symptomatic neutralizercontaining a numerical score; selecting a default portion size;comparing the numerical score to the default portion size; and adjustingthe portion size as a function of the symptomatic neutralizer.
 10. Amethod for ordered food preferences accompanying symptomatic inputs, themethod comprising: retrieving by a computing device, a food profilepertaining to a user; selecting by the computing device, a first foodelement as a function of the food profile; selecting by the computingdevice, a second food element as a function of the first food element;creating by the computing device, a food preference menu wherein thefood preference menu contains the first food element and the second foodelement, wherein creating the food preference menu comprises:identifying a previous meal selection; classifying, using a Naïve Bayesclassifier derived from training data comprising meal selections datacorrelated to meal category data, the previous meal selection to a mealcategory; and creating the food preference menu as a function of themeal category; and modifying by the computing device, the foodpreference menu as a function of an entry contained within a symptomaticdatabase.
 11. The method of claim 10, wherein retrieving the foodprofile further comprises retrieving the food profile as a function of agenetically related food preference.
 12. The method of claim 10, whereinselecting the first food element further comprises selecting the firstfood element as a function of a user taste preference.
 13. The method ofclaim 10, wherein selecting the first food element further comprisesselecting the first food element as a function of a symptomatic input.14. The method of claim 10, wherein selecting the second food elementfurther comprises selecting the second food element to enhance thenutrition of the first food element.
 15. The method of claim 10, whereincreating the food preference menu further comprises generating amachine-learning process, wherein the machine-learning process utilizesthe food profile as an input, and outputs the food preference menu. 16.The method of claim 15, wherein generating the machine-learning processfurther comprises generating a feature learning process.
 17. The methodof claim 10, wherein modifying the food preference menu furthercomprises adjusting a portion size contained within a nutritional plan.18. The method of claim 17, wherein adjusting the portion size furthercomprises: locating a symptomatic neutralizer containing a numericalscore; selecting a default portion size; comparing the numerical scoreto the default portion size; and adjusting the portion size as afunction of the symptomatic neutralizer.